A Vulnerability Detection System Based on Fusion of Assembly Code and Source Code
| Autor: | Mingjin He, Tong Li, Bingwen Feng, Xingzheng Li, Guofeng Li |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Source code
Science (General) Article Subject Computer Networks and Communications Computer science media_common.quotation_subject 0211 other engineering and technologies 02 engineering and technology Intrusion Q1-390 Software 020204 information systems 0202 electrical engineering electronic engineering information engineering Code (cryptography) T1-995 Technology (General) media_common computer.programming_language 021110 strategic defence & security studies Fusion Assembly language business.industry Deep learning Vulnerability detection Computer engineering Artificial intelligence business computer Information Systems |
| Zdroj: | Security and Communication Networks, Vol 2021 (2021) |
| ISSN: | 1939-0122 1939-0114 |
| Popis: | Software vulnerabilities are one of the important reasons for network intrusion. It is vital to detect and fix vulnerabilities in a timely manner. Existing vulnerability detection methods usually rely on single code models, which may miss some vulnerabilities. This paper implements a vulnerability detection system by combining source code and assembly code models. First, code slices are extracted from the source code and assembly code. Second, these slices are aligned by the proposed code alignment algorithm. Third, aligned code slices are converted into vector and input into a hyper fusion-based deep learning model. Experiments are carried out to verify the system. The results show that the system presents a stable and convergent detection performance. |
| Databáze: | OpenAIRE |
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